This episode of Humans of ML is special because Emil got his start in AI at FloydHub. In , Emil created a popular open-source project that translates design mock-ups into HTML / CSS, Screenshot-to-code. In early , he was the subject of a short film made by Google for his work on automated colorization . He previously worked for the University of Oxford. He co-founded a seed investment firm that focuses on education technology.
From My travels, I was exposed to a lot of social issues that led me into social entrepreneurship in my mid-twenties. I started working with the Skoll Center for Social Entrepreneurship at the University of Oxford. One thing led to another, and I ended up co-founding an investment firm to fund education initiatives.
During my internship, I spent my first two months playing with models and implemented the core deep learning algorithms from scratch. I then spent two months colorizing images with neural networks, and finished my internship with a project to translate design mock-ups into HTML. You can read about what I did on the FloydHub blog . The the launch of the AI phase of my career.
Can you elaborate more on the signaling in the self-taught process? In other words, how can we recognize when we are on the right track or pursuing the right learning experience? Creating value with your knowledge is evidence of learning. I see learning as a by-product of trying to achieve an intrinsic goal, rather than an isolated activity to become educated.
At large companies, less than a few percent are self-taught in ML. Of those, most don’t come through the classic hiring channels. Due to the volume of university applicants a large company faces, it’s harder for them to adjust to portfolio-centric hiring. It’s not an easy problem, here are the rough guidelines I shared earlier:
) What I’ve seen work is getting good at a niche and letting the world know about it. Have a blog, be active on Twitter, and engage with researchers via email.
any advice for creating an AI portfolio?
Once an employer checks your portfolio, you have 19 seconds to pique their interest and another 40 seconds to convince them you are a fit.
Novelty has high credentialism-value because it’s evidence that you have unique knowledge and it’s clear that it came from you. Reproducing a paper without code is evidence that you can understand machine learning papers. And creating an in-depth blog post about your work creates further evidence that you made a genuine contribution.
) To create additional evidence, you can engage in an objective process to assess your work, in the form of machine learning competitions, publishing papers, or sharing it online to see what the broader public thinks of your work. Formal research is often measured by publishing first-author papers in high-quality conferences or journals.
What Sutton pointed out was that the best models tend to have few priors. But most if not all of today’s building blocks in AI could have been invented with small compute. The industry labs have a significant advantage when it comes to applying these in the real world, but that is mostly a concern for the industry as of now.
Because big labs tend to do large scale projects with significant PR efforts, that’s what most end up talking about. As a result, that’s what becomes trendy.
Great advice. What are interesting AI research areas that don’t require too much compute? I agree with François (Chollet’s ‘The Measure of Intelligence.’ We should shift from solving tasks to models that are concerned with skill-acquisition efficiency, ie systems that can solve a broad set of tasks that involve uncertainty. Today’s systems lead to local-generalization and there is little evidence that they are useful for human-like intelligence.
A lot of this has to do with areas related to reasoning, here’s what I outlined earlier:
). Networks that break a problem into intermediate logic and create specialized models for each step. Unsupervised and curriculum learning will be important to develop Chollet’s idea of ’Core Knowledge’, a core set of priors to enable reasoning.
Yes. I think consciousness is a spectrum and is commonly thought of as the point when we become self-aware, which happens roughly at the age of two for humans. It’s a point when we have enough general and high-level abstractions of the world to start forming a coherent identity.
That leads to another question, how do you develop an identity when you don’t have constraints such as nature and nurture? You can artificially create constraints to create the illusion of a human-like identity, but artificial identities are probably going to evolve from information and energy constraints.
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